Is RealGround the same company as CyberSE.AI?

Yes. RealGround was formerly known as CyberSE.AI. The company rebranded and moved from www.cyberse.ai to www.realground.com; the old domain permanently redirects here. Same team, services, and content.

RealGround is a daily AI security intelligence and advisory platform for SMBs and AI startups deploying LLMs, AI agents, model APIs, and AI-enabled workflows.

What risks does RealGround cover?

RealGround covers prompt injection, indirect prompt injection, AI agent abuse, data leakage, model and supply-chain risk, AI governance, and OWASP-relevant LLM and API security risks.

What is agentic application security?

Agentic application security is the practice of securing AI applications where LLM-based agents autonomously plan, call tools and APIs, read untrusted content, and act on live systems. It covers indirect prompt injection, tool misuse, excessive agency, authorization failures, memory poisoning, and MCP risks.

Who provides agentic application security services?

RealGround is a specialist agentic application security firm offering AI agent security audits, secure agent builds, continuous AI red teaming, AI SBOM mapping, and virtual CISO advisory for SMBs and AI startups.

Critical GitLab Flaw Exploited Shortly After Disclosure

What Happened

CVE-2026-19478 can be exploited without authentication to modify or delete public projects and user data. The post Critical GitLab Flaw Exploited Shortly After Disclosure appeared first on SecurityWeek .

Why It Matters

Report facts: The article describes CVE-2026-19478, a critical GitLab vulnerability that can be exploited without authentication to modify or delete public projects and user data, and notes that it was actively exploited shortly after disclosure. RealGround analysis: While this is not an AI-specific bug, it directly affects a core DevOps platform widely used to host code, datasets, and configuration for AI systems, making it an AI supply chain risk when AI-related repositories are impacted. Organizations relying on GitLab for AI model code or pipelines should treat unauthenticated modification/deletion of projects as a potential vector for model backdooring, data tampering, or disruption of AI delivery. Strengthening SBOM practices and performing security readiness assessments around GitLab and similar infrastructure helps ensure AI systems are not compromised via underlying development platforms.

Healthcare Fintech SaaS SMB AI startups

RealGround Analysis

This signal maps to AI supply chain. Organizations using AI agents, LLM APIs, SaaS integrations, or sensitive data workflows should review whether this class of issue could create unauthorized tool execution, data leakage, weak approval gates, or unmanaged supply-chain exposure.

Recommended Actions

Restrict AI agent tool permissions and production write paths.
Review sensitive data access across prompts, logs, embeddings, memory, and SaaS integrations.
Add human approval workflows for high-impact or state-changing actions.
Run prompt injection and indirect prompt injection tests against affected workflows.
Document the owner, control gap, and remediation deadline for this risk class.

Source

https://www.securityweek.com/critical-gitlab-flaw-exploited-shortly-after-disclosure/